text: Folding funnel theory has both qualitative and quantitative application. Visualization of funnels creates a communicating tool between statistical mechanical properties of proteins and their folding kinetics. It suggests the stability of folding process, which would be hard to destroy by mutation given maintained stability. To be more specific, a mutation can occur that leads to blockage of a routes to native state, but another route can take over provided that it reaches the final structure.
  A protein's stability increases as it approaches its native state through the partially folded configuration. Local structures such as helices and turns happen first followed by global assembly. Despite a process of trial and error, protein folding can be fast because proteins reach its native structure by this divide-and-conquer, local-to-global process. The idea of folding funnel helps rationalize the purpose of chaperones, in which the re-folding process of a protein can be catalyzed by chaperones pulling it apart and bringing it to a high energy landscape and let it fold again in a random fashion of trials and errors. Funneled landscapes suggest that different individual molecules of the same protein sequence may utilize microscopically different routes to reach the same destination. Some paths will be more populated than others.
  Funnels distinguish the basics between folding and simple classical chemical reactions analogy. A chemical reaction starts from its reactant A and goes through a change in structure to reach its product B. On the other hand, folding is a transition from disorder to order, not only from structure to structure. Simple one-dimensional reaction pathway does not capture protein foldings reduction in conformational degeneracy. In other words, folding funnels provide a microscopic framework for folding kinetics. Folding kinetics is described by simple mass action models, D-I-N (on-path intermediate I between denatured D and native N) or X-D-N (off-path intermediate X), and is referred to as the macroscopic framework of folding. Sequential Micropath view represents the mass-action model and explains folding kinetics in terms of pathways, transition states, on and off-path intermediates and what one sees in experiments, and is not concerned with the activity of a molecule or the state of a monomer sequence at a specific macroscopic transition state. Its problem is related to Levinthals Paradox, or the searching problem. In contrast, funnel models aim to explain the kinetics in terms of underlying physical forces, to predict the microstate composition of those macrostates.
  Nonetheless, it proves challenging for computer simulations (energy landscape) to reconcile the "macroscopic" view of mass-action models with "microscopic" understanding of the changes in protein conformation during the folding process. Insights from funnels are not sufficient enough to improve computer search methods. A smooth and funnel-shaped landscape on global scale can appear rough on local scale in computer simulations.